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HTC is gone

Gather around, campers, and hear a tale as old as time.

Remember the HTC Dream? The Evo 4G? The Google Nexus One? What about the Touch Diamond? All amazing devices. The HTC of 2018 is not the HTC that made these industry-leading devices. That company is gone.

It seems HTC is getting ready to lay off nearly a quarter of its workforce by cutting 1,500 jobs in its manufacturing unit in Taiwan. After the cuts, HTC’s employee count will be less than 5,000 people worldwide. Five years ago, in 2013, HTC employed 19,000 people.

HTC started as a white label device maker giving carriers an option to sell devices branded with their name. The company also had a line of HTC-branded connected PDAs that competed in the nascent smartphone market. BlackBerry, or Research in Motion as it was called until 2013, ruled this phone segment, but starting around 2007 HTC began making inroads thanks to innovated touch devices that ran Windows Mobile 6.0.

In 2008 HTC introduced the Touch line with the Touch Diamond, Touch Pro, Touch 3G and Touch HD. These were stunning devices for the time. They were fast, loaded with big, user swappable batteries and microSD card slots. The Touch Pro even had a front-facing camera for video calls.

HTC overlayed a custom skin onto Windows Mobile making it a bit more palatable for the general user. At that time, Windows Mobile was competing with BlackBerry’s operating system and Nokia’s Symbian. None was fantastic, but Windows Mobile was by far the most daunting for new users. HTC did the best thing it could do and developed a smart skin that gave the phone a lot of features that would still be considered modern.

In 2009 HTC released the first Android device with Google. Called the HTC Dream or G1, the device was far from perfect. But the same could be said about the iPhone. This first Android phone set the stage for future wins from HTC, too. The company quickly followed up with the Hero, Droid Incredible, Evo 4G and, in 2010, the amazing Google Nexus One.

After the G1, HTC started skinning Android in the same fashion as it did Windows Mobile. It cannot be overstated how important this was for the adoption of Android. HTC’s user interface made Android usable and attractive. HTC helped make Android a serious competitor to Apple’s iOS.

In 2010 and 2011, Google turned to Samsung to make the second and third flagship Nexus phones. It was around this time Samsung started cranking out Android phones, and HTC couldn’t keep up. That’s not to say HTC didn’t make a go for it. The company kept releasing top-tier phones: the One X in 2012, the One Max in 2013 and the One (M8) in 2014. But it didn’t matter. Samsung had taken up the Android standard and was charging forward, leaving HTC, Sony and LG to pick from the scraps.

At the end of 2010, HTC was the leading smartphone vendor in the United States. In 2014 it trailed Apple, Samsung and LG with around a 6 percent market share in the U.S. In 2017 HTC captured 2.3 percent of smartphone subscribers and now in 2018, some reports peg HTC with less than a half percent of the smartphone market.

Google purchased a large chunk of HTC’s smartphone design talent in 2017 for $1.1 billion. The deal transferred more than 2,000 employees under Google’s tutelage. They will likely be charged with working on Google’s line of Pixel devices. It’s a smart move. This HTC team was responsible for releasing amazing devices that no one bought. But that’s not entirely their fault. Outside forces are to blame. HTC never stopped making top-tier devices.

The HTC of today is primarily focused on the Vive product line. And that’s a smart play. The HTC Vive is one of the best virtual reality platforms available. But HTC has been here before. Hopefully, it learned something from its mistakes in smartphones.

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Apple is rebuilding Maps from the ground up

I’m not sure if you’re aware, but the launch of Apple Maps went poorly. After a rough first impression, an apology from the CEO, several years of patching holes with data partnerships and some glimmers of light with long-awaited transit directions and improvements in business, parking and place data, Apple Maps is still not where it needs to be to be considered a world-class service.

Maps needs fixing.

Apple, it turns out, is aware of this, so it’s re-building the maps part of Maps.

It’s doing this by using first-party data gathered by iPhones with a privacy-first methodology and its own fleet of cars packed with sensors and cameras. The new product will launch in San Francisco and the Bay Area with the next iOS 12 beta and will cover Northern California by fall.

Every version of iOS will get the updated maps eventually, and they will be more responsive to changes in roadways and construction, more visually rich depending on the specific context they’re viewed in and feature more detailed ground cover, foliage, pools, pedestrian pathways and more.

This is nothing less than a full re-set of Maps and it’s been four years in the making, which is when Apple began to develop its new data-gathering systems. Eventually, Apple will no longer rely on third-party data to provide the basis for its maps, which has been one of its major pitfalls from the beginning.

“Since we introduced this six years ago — we won’t rehash all the issues we’ve had when we introduced it — we’ve done a huge investment in getting the map up to par,” says Apple SVP Eddy Cue, who now owns Maps, in an interview last week. “When we launched, a lot of it was all about directions and getting to a certain place. Finding the place and getting directions to that place. We’ve done a huge investment of making millions of changes, adding millions of locations, updating the map and changing the map more frequently. All of those things over the past six years.”

But, Cue says, Apple has room to improve on the quality of Maps, something that most users would agree on, even with recent advancements.

“We wanted to take this to the next level,” says Cue. “We have been working on trying to create what we hope is going to be the best map app in the world, taking it to the next step. That is building all of our own map data from the ground up.”

In addition to Cue, I spoke to Apple VP Patrice Gautier and more than a dozen Apple Maps team members at its mapping headquarters in California this week about its efforts to re-build Maps, and to do it in a way that aligned with Apple’s very public stance on user privacy.

If, like me, you’re wondering whether Apple thought of building its own maps from scratch before it launched Maps, the answer is yes. At the time, there was a choice to be made about whether or not it wanted to be in the business of maps at all. Given that the future of mobile devices was becoming very clear, it knew that mapping would be at the core of nearly every aspect of its devices, from photos to directions to location services provided to apps. Decision made, Apple plowed ahead, building a product that relied on a patchwork of data from partners like TomTom, OpenStreetMap and other geo data brokers. The result was underwhelming.

Almost immediately after Apple launched Maps, it realized that it was going to need help and it signed on a bunch of additional data providers to fill the gaps in location, base map, point-of-interest and business data.

It wasn’t enough.

“We decided to do this just over four years ago. We said, ‘Where do we want to take Maps? What are the things that we want to do in Maps?’ We realized that, given what we wanted to do and where we wanted to take it, we needed to do this ourselves,” says Cue.

Because Maps are so core to so many functions, success wasn’t tied to just one function. Maps needed to be great at transit, driving and walking — but also as a utility used by apps for location services and other functions.

Cue says that Apple needed to own all of the data that goes into making a map, and to control it from a quality as well as a privacy perspective.

There’s also the matter of corrections, updates and changes entering a long loop of submission to validation to update when you’re dealing with external partners. The Maps team would have to be able to correct roads, pathways and other updating features in days or less, not months. Not to mention the potential competitive advantages it could gain from building and updating traffic data from hundreds of millions of iPhones, rather than relying on partner data.

Cue points to the proliferation of devices running iOS, now over a billion, as a deciding factor to shift its process.

“We felt like because the shift to devices had happened — building a map today in the way that we were traditionally doing it, the way that it was being done — we could improve things significantly, and improve them in different ways,” he says. “One is more accuracy. Two is being able to update the map faster based on the data and the things that we’re seeing, as opposed to driving again or getting the information where the customer’s proactively telling us. What if we could actually see it before all of those things?”

I query him on the rapidity of Maps updates, and whether this new map philosophy means faster changes for users.

“The truth is that Maps needs to be [updated more], and even are today,” says Cue. “We’ll be doing this even more with our new maps, [with] the ability to change the map in real time and often. We do that every day today. This is expanding us to allow us to do it across everything in the map. Today, there’s certain things that take longer to change.

“For example, a road network is something that takes a much longer time to change currently. In the new map infrastructure, we can change that relatively quickly. If a new road opens up, immediately we can see that and make that change very, very quickly around it. It’s much, much more rapid to do changes in the new map environment.”

So a new effort was created to begin generating its own base maps, the very lowest building block of any really good mapping system. After that, Apple would begin layering on living location data, high-resolution satellite imagery and brand new intensely high-resolution image data gathered from its ground cars until it had what it felt was a “best in class” mapping product.

There is only really one big company on earth that owns an entire map stack from the ground up: Google .

Apple knew it needed to be the other one. Enter the vans.

Apple vans spotted

Though the overall project started earlier, the first glimpse most folks had of Apple’s renewed efforts to build the best Maps product was the vans that started appearing on the roads in 2015 with “Apple Maps” signs on the side. Capped with sensors and cameras, these vans popped up in various cities and sparked rampant discussion and speculation.

The new Apple Maps will be the first time the data collected by these vans is actually used to construct and inform its maps. This is their coming out party.

Some people have commented that Apple’s rigs look more robust than the simple GPS + Camera arrangements on other mapping vehicles — going so far as to say they look more along the lines of something that could be used in autonomous vehicle training.

Apple isn’t commenting on autonomous vehicles, but there’s a reason the arrays look more advanced: they are.

Earlier this week I took a ride in one of the vans as it ran a sample route to gather the kind of data that would go into building the new maps. Here’s what’s inside.

In addition to a beefed-up GPS rig on the roof, four LiDAR arrays mounted at the corners and eight cameras shooting overlapping high-resolution images, there’s also the standard physical measuring tool attached to a rear wheel that allows for precise tracking of distance and image capture. In the rear there is a surprising lack of bulky equipment. Instead, it’s a straightforward Mac Pro bolted to the floor, attached to an array of solid state drives for storage. A single USB cable routes up to the dashboard where the actual mapping-capture software runs on an iPad.

While mapping, a driver…drives, while an operator takes care of the route, ensuring that a coverage area that has been assigned is fully driven, as well as monitoring image capture. Each drive captures thousands of images as well as a full point cloud (a 3D map of space defined by dots that represent surfaces) and GPS data. I later got to view the raw data presented in 3D and it absolutely looks like the quality of data you would need to begin training autonomous vehicles.

More on why Apple needs this level of data detail later.

When the images and data are captured, they are then encrypted on the fly and recorded on to the SSDs. Once full, the SSDs are pulled out, replaced and packed into a case, which is delivered to Apple’s data center, where a suite of software eliminates from the images private information like faces, license plates and other info. From the moment of capture to the moment they’re sanitized, they are encrypted with one key in the van and the other key in the data center. Technicians and software that are part of its mapping efforts down the pipeline from there never see unsanitized data.

This is just one element of Apple’s focus on the privacy of the data it is utilizing in New Maps.

Probe data and privacy

Throughout every conversation I have with any member of the team throughout the day, privacy is brought up, emphasized. This is obviously by design, as Apple wants to impress upon me as a journalist that it’s taking this very seriously indeed, but it doesn’t change the fact that it’s evidently built in from the ground up and I could not find a false note in any of the technical claims or the conversations I had.

Indeed, from the data security folks to the people whose job it is to actually make the maps work well, the constant refrain is that Apple does not feel that it is being held back in any way by not hoovering every piece of customer-rich data it can, storing and parsing it.

The consistent message is that the team feels it can deliver a high-quality navigation, location and mapping product without the directly personal data used by other platforms.

“We specifically don’t collect data, even from point A to point B,” notes Cue. “We collect data — when we do it — in an anonymous fashion, in subsections of the whole, so we couldn’t even say that there is a person that went from point A to point B. We’re collecting the segments of it. As you can imagine, that’s always been a key part of doing this. Honestly, we don’t think it buys us anything [to collect more]. We’re not losing any features or capabilities by doing this.”

The segments that he is referring to are sliced out of any given person’s navigation session. Neither the beginning or the end of any trip is ever transmitted to Apple. Rotating identifiers, not personal information, are assigned to any data or requests sent to Apple and it augments the “ground truth” data provided by its own mapping vehicles with this “probe data” sent back from iPhones.

Because only random segments of any person’s drive is ever sent and that data is completely anonymized, there is never a way to tell if any trip was ever a single individual. The local system signs the IDs and only it knows to whom that ID refers. Apple is working very hard here to not know anything about its users. This kind of privacy can’t be added on at the end, it has to be woven in at the ground level.

Because Apple’s business model does not rely on it serving to you, say, an ad for a Chevron on your route, it doesn’t need to even tie advertising identifiers to users.

Any personalization or Siri requests are all handled on-board by the iOS device’s processor. So if you get a drive notification that tells you it’s time to leave for your commute, that’s learned, remembered and delivered locally, not from Apple’s servers.

That’s not new, but it’s important to note given the new thing to take away here: Apple is flipping on the power of having millions of iPhones passively and actively improving their mapping data in real time.

In short: Traffic, real-time road conditions, road systems, new construction and changes in pedestrian walkways are about to get a lot better in Apple Maps.

The secret sauce here is what Apple calls probe data. Essentially little slices of vector data that represent direction and speed transmitted back to Apple completely anonymized with no way to tie it to a specific user or even any given trip. It’s reaching in and sipping a tiny amount of data from millions of users instead, giving it a holistic, real-time picture without compromising user privacy.

If you’re driving, walking or cycling, your iPhone can already tell this. Now if it knows you’re driving, it also can send relevant traffic and routing data in these anonymous slivers to improve the entire service. This only happens if your Maps app has been active, say you check the map, look for directions, etc. If you’re actively using your GPS for walking or driving, then the updates are more precise and can help with walking improvements like charting new pedestrian paths through parks — building out the map’s overall quality.

All of this, of course, is governed by whether you opted into location services, and can be toggled off using the maps location toggle in the Privacy section of settings.

Apple says that this will have a near zero effect on battery life or data usage, because you’re already using the ‘maps’ features when any probe data is shared and it’s a fraction of what power is being drawn by those activities.

From the point cloud on up

But maps cannot live on ground truth and mobile data alone. Apple is also gathering new high-resolution satellite data to combine with its ground truth data for a solid base map. It’s then layering satellite imagery on top of that to better determine foliage, pathways, sports facilities, building shapes and pathways.

After the downstream data has been cleaned up of license plates and faces, it gets run through a bunch of computer vision programming to pull out addresses, street signs and other points of interest. These are cross referenced to publicly available data like addresses held by the city and new construction of neighborhoods or roadways that comes from city planning departments.

But one of the special sauce bits that Apple is adding to the mix of mapping tools is a full-on point cloud that maps in 3D the world around the mapping van. This allows them all kinds of opportunities to better understand what items are street signs (retro-reflective rectangular object about 15 feet off the ground? Probably a street sign) or stop signs or speed limit signs.

It seems like it also could enable positioning of navigation arrows in 3D space for AR navigation, but Apple declined to comment on “any future plans” for such things.

Apple also uses semantic segmentation and Deep Lambertian Networks to analyze the point cloud coupled with the image data captured by the car and from high-resolution satellites in sync. This allows 3D identification of objects, signs, lanes of traffic and buildings and separation into categories that can be highlighted for easy discovery.

The coupling of high-resolution image data from car and satellite, plus a 3D point cloud, results in Apple now being able to produce full orthogonal reconstructions of city streets with textures in place. This is massively higher-resolution and easier to see, visually. And it’s synchronized with the “panoramic” images from the car, the satellite view and the raw data. These techniques are used in self-driving applications because they provide a really holistic view of what’s going on around the car. But the ortho view can do even more for human viewers of the data by allowing them to “see” through brush or tree cover that would normally obscure roads, buildings and addresses.

This is hugely important when it comes to the next step in Apple’s battle for supremely accurate and useful Maps: human editors.

Apple has had a team of tool builders working specifically on a toolkit that can be used by human editors to vet and parse data, street by street. The editor’s suite includes tools that allow human editors to assign specific geometries to flyover buildings (think Salesforce tower’s unique ridged dome) that allow them to be instantly recognizable. It lets editors look at real images of street signs shot by the car right next to 3D reconstructions of the scene and computer vision detection of the same signs, instantly recognizing them as accurate or not.

Another tool corrects addresses, letting an editor quickly move an address to the center of a building, determine whether they’re misplaced and shift them around. It also allows for access points to be set, making Apple Maps smarter about the “last 50 feet” of your journey. You’ve made it to the building, but what street is the entrance actually on? And how do you get into the driveway? With a couple of clicks, an editor can make that permanently visible.

“When we take you to a business and that business exists, we think the precision of where we’re taking you to, from being in the right building,” says Cue. “When you look at places like San Francisco or big cities from that standpoint, you have addresses where the address name is a certain street, but really, the entrance in the building is on another street. They’ve done that because they want the better street name. Those are the kinds of things that our new Maps really is going to shine on. We’re going to make sure that we’re taking you to exactly the right place, not a place that might be really close by.”

Water, swimming pools (new to Maps entirely), sporting areas and vegetation are now more prominent and fleshed out thanks to new computer vision and satellite imagery applications. So Apple had to build editing tools for those, as well.

Many hundreds of editors will be using these tools, in addition to the thousands of employees Apple already has working on maps, but the tools had to be built first, now that Apple is no longer relying on third parties to vet and correct issues.

And the team also had to build computer vision and machine learning tools that allow it to determine whether there are issues to be found at all.

Anonymous probe data from iPhones, visualized, looks like thousands of dots, ebbing and flowing across a web of streets and walkways, like a luminescent web of color. At first, chaos. Then, patterns emerge. A street opens for business, and nearby vessels pump orange blood into the new artery. A flag is triggered and an editor looks to see if a new road needs a name assigned.

A new intersection is added to the web and an editor is flagged to make sure that the left turn lanes connect correctly across the overlapping layers of directional traffic. This has the added benefit of massively improved lane guidance in the new Apple Maps.

Apple is counting on this combination of human and AI flagging to allow editors to first craft base maps and then also maintain them as the ever-changing biomass wreaks havoc on roadways, addresses and the occasional park.

Here there be Helvetica

Apple’s new Maps, like many other digital maps, display vastly differently depending on scale. If you’re zoomed out, you get less detail. If you zoom in, you get more. But Apple has a team of cartographers on staff that work on more cultural, regional and artistic levels to ensure that its Maps are readable, recognizable and useful.

These teams have goals that are at once concrete and a bit out there — in the best traditions of Apple pursuits that intersect the technical with the artistic.

The maps need to be usable, but they also need to fulfill cognitive goals on cultural levels that go beyond what any given user might know they need. For instance, in the U.S., it is very common to have maps that have a relatively low level of detail even at a medium zoom. In Japan, however, the maps are absolutely packed with details at the same zoom, because that increased information density is what is expected by users.

This is the department of details. They’ve reconstructed replicas of hundreds of actual road signs to make sure that the shield on your navigation screen matches the one you’re seeing on the highway road sign. When it comes to public transport, Apple licensed all of the type faces that you see on your favorite subway systems, like Helvetica for NYC. And the line numbers are in the exact same order that you’re going to see them on the platform signs.

It’s all about reducing the cognitive load that it takes to translate the physical world you have to navigate into the digital world represented by Maps.

Bottom line

The new version of Apple Maps will be in preview next week with just the Bay Area of California going live. It will be stitched seamlessly into the “current” version of Maps, but the difference in quality level should be immediately visible based on what I’ve seen so far.

Better road networks, more pedestrian information, sports areas like baseball diamonds and basketball courts, more land cover, including grass and trees, represented on the map, as well as buildings, building shapes and sizes that are more accurate. A map that feels more like the real world you’re actually traveling through.

Search is also being revamped to make sure that you get more relevant results (on the correct continents) than ever before. Navigation, especially pedestrian guidance, also gets a big boost. Parking areas and building details to get you the last few feet to your destination are included, as well.

What you won’t see, for now, is a full visual redesign.

“You’re not going to see huge design changes on the maps,” says Cue. “We don’t want to combine those two things at the same time because it would cause a lot of confusion.”

Apple Maps is getting the long-awaited attention it really deserves. By taking ownership of the project fully, Apple is committing itself to actually creating the map that users expected of it from the beginning. It’s been a lingering shadow on iPhones, especially, where alternatives like Google Maps have offered more robust feature sets that are so easy to compare against the native app but impossible to access at the deep system level.

The argument has been made ad nauseam, but it’s worth saying again that if Apple thinks that mapping is important enough to own, it should own it. And that’s what it’s trying to do now.

“We don’t think there’s anybody doing this level of work that we’re doing,” adds Cue. “We haven’t announced this. We haven’t told anybody about this. It’s one of those things that we’ve been able to keep pretty much a secret. Nobody really knows about it. We’re excited to get it out there. Over the next year, we’ll be rolling it out, section by section in the U.S.”

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India’s Cashify raises $12M for its second-hand smartphone business

Cashify, a company that buys and sells used smartphones, is the latest India startup to raise capital from Chinese investors after it announced a $12 million Series C round.

Chinese funds CDH Investments and Morningside led the round, which included participation from Aihuishou, a China-based startup that sells used electronics in a similar way to Cashify and has raised more than $120 million. Existing investors, including Bessemer Ventures and Shunwei, also took part in the round.

This new capital takes Cashify to $19 million raised to date.

The business was started in 2013 by co-founders Mandeep Manocha (CEO), Nakul Kumar (COO) and Amit Sethi (CTO) initially as ReGlobe. The business gives consumers a fast way to sell their existing electronics; it deals mainly in smartphones but also takes laptops, consoles, TVs and tablets.

“When we began we saw a lot of transaction for phone sales moving from offline to online,” Manocha told TechCrunch in an interview. “But consumer-to-consumer [for used devices] is highly opaque on price discovery and you never know if you’re making the right decision on price and whether the transaction will take place in the timeframe.”

These days, the company estimates that the average upgrade cycle has shifted from 20 months to 12 months, and now it is doubling down.

With Cashify, sellers simply fill out some details online about their device, then Cashify dispatches a representative who comes to their house to perform diagnostic checks and gives them cash for the device that day. The startup also offers an app which automatically carries out the checks — for example ensuring the camera, Bluetooth module, etc. all work — and offers a higher cash payment for the user since Cashify uses fewer resources.

A sample of the Cashify Q&A for selling a device

Beyond its website and app, Cashify gets devices from trade-in programs for Samsung, Xiaomi and Apple in India, as well as e-commerce companies like Flipkart, Amazon and Paytm Mall.

Used device acquired, what happens next is interesting.

The startup has built out a network of offline merchants who specialize in selling used phones. Each phone it acquires is then sold (perhaps after minor refurbishments) to that network, so it might pop up for sale anywhere in India.

With this new money, Cashify CEO Manocha said the company will develop an online resale site that will allow anyone to buy a used phone from the company’s network. Devices sold by Cashify online will be refurbished with new parts where needed, and they’ll include a box and six-month warranty to give a better consumer experience, Manocha added.

Today, Cashify claims to handle 100,000 smartphones a month, but it is planning to grow that to 200,000 by the end of this year. Cashify said its devices are typically low-end, those that retail for sub-$300 when new. A large part of that push comes from the online site, but the startup is also enlarging its offline merchant network and working to reach more consumers who are actually selling their device. That’s where Manocha said he sees particular value in working with Aihuishou.

Cashify is also developing other services. It recently started offering at-home repairs for customers and Manocha said that adding Chinese investors — and Aihuishou in particular — will help it with its sourcing of components for the repairs service and general refurbishments.

Cashify estimates that the used smartphone market in India will see 90 million phones sold this year, with as many as 120 million trading by 2020. That’s close to the 124 million shipments that analysts estimate India saw in 2017, but with surprisingly higher margins.

A reseller can make 10 percent profit on a device, Manocha explained, and Cashify’s own price elasticity — the difference between what it buys from consumers at and what it sells to resellers for — is typically 30-35 percent, he added. That’s more than most OEMs, but that doesn’t take into account costs on the Cashify side, which bring that number down.

“When I sell to a reseller, the margins aren’t that exciting, which is why we want to sell direct to consumers,” the Cashify CEO said.

The startup has plenty going on at home in India, but already it is considering overseas possibilities.

“We will focus on India for at least the next 12 months, but we have had discussions on markets that would make sense to enter,” Manocha said, explaining that the Middle East and Southeast Asia are early frontrunners.

“We are working very closely with one of the Chinese players and figuring out if we can do some business in Hong Kong because that’s the hub for second-hand phones in this part of the world,” he added.

Note: The original version of this article was updated to correct that Amit Sethi is CTO not CFO.

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The well-funded startups driven to own the autonomous vehicle stack

At some point in the future, while riding along in a car, a kid may ask their parent about a distant time in the past when people used steering wheels and pedals to control an automobile. Of course, the full realization of the “auto” part of the word — in the form of fully autonomous automobiles — is a long way off, but there are nonetheless companies trying to build that future today.

However, changing the face of transportation is a costly business, one that typically requires corporate backing or a lot of venture funding to realize such an ambitious goal. A recent funding round, some $128 million raised in a Series A round by Shenzhen-based Roadstar.ai, got us at Crunchbase News asking a question: Just how many independent, well-funded autonomous vehicles startups are out there?

In short, not as many as you’d think. To investigate further, we took a look at the set of independent companies in Crunchbase’s “autonomous vehicle” category that have raised $50 million or more in venture funding. After a little bit of hand filtering, we found that the companies mostly shook out into two broad categories: those working on sensor technologies, which are integral to any self-driving system, and more “full-stack” hardware and software companies, which incorporate sensors, machine-learned software models and control mechanics into more integrated autonomous systems.

Full-stack self-driving vehicle companies

Let’s start with full-stack companies first. The table below shows the set of independent full-stack autonomous vehicle companies operating in the market today, as well as their focus areas, headquarter’s location and the total amount of venture funding raised:

Note the breakdown in focus area between the companies listed above. In general, these companies are focused on building more generalized technology platforms — perhaps to sell or license to major automakers in the future — whereas others intend to own not just the autonomous car technology, but deploy it in a fleet of on-demand taxi and other transportation services.

Making the eyes and ears of autonomous vehicles

On the sensor side, there is also a trend, one that’s decidedly more concentrated on one area of focus, as you’ll be able to discern from the table below:

Some of the most well-funded startups in the sensing field are developing light detection and ranging (LiDAR) technologies, which basically serve as the depth-perceiving “eyes” of autonomous vehicle systems. CYNGN integrates a number of different sensors, LiDAR included, into its hardware arrays and software tools, which is one heck of a pivot for the mobile phone OS-maker formerly known as Cyanogen.

But there are other problem spaces for these sensor companies, including Nauto’s smart dashcam, which gathers location data and detects distracted driving, or Autotalks’s DSRC technology for vehicle-to-vehicle communication. (Back in April, Crunchbase News covered the $5 million Series A round closed by Comma, which released an open-source dashcam app.)

And unlike some of the full-stack providers mentioned earlier, many of these sensor companies have established vendor relationships with the automotive industry. Quanergy Systems, for example, counts components giant Delphi, luxury carmakers Jaguar and Mercedes-Benz and automakers like Hyundai and Renault-Nissan as partners and investorsInnoviz supplies its solid-state LiDAR technology to the BMW Group, according to its website.

Although radar and even LiDAR are old hat by now, there continues to be innovation in sensors. According to a profile of Oryx Vision’s technology in IEEE Spectrum, its “coherent optical radar” system is kind of like a hybrid of radar and LiDAR technology in that “it uses a laser to illuminate the road ahead [with infrared light], but like a radar it treats the reflected signal as a wave rather than a particle.” Its technology is able to deliver higher-resolution sensing over a longer distance than traditional radar or newer LiDAR technologies.

Can startups stack up against big corporate competitors?

There are plenty of autonomous vehicle initiatives backed by deep corporate pockets. There’s Waymo, a subsidiary of Alphabet, which is subsidized by the huge amount of search profit flung off by Google . Uber has an autonomous vehicles initiative too, although it has encountered a whole host of legal and safety issues, including holding the unfortunate distinction of being the first to kill a pedestrian earlier this year.

Tesla, too, has invested considerable resources into developing assistive technologies for its vehicles, but it too has encountered some roadblocks as its head of Autopilot (its in-house autonomy solution) left in April. The company also deals with a rash of safety concerns of its own. And although Apple’s self-driving car program has been less publicized than others, it continues to roll on in the background. Chinese companies like Baidu and Didi Chuxing have also launched fill-stack R&D facilities in Silicon Valley.

Traditional automakers have also jumped into the fray. Back in 2016, for the price of a cool $1 billion, General Motors folded Cruise Automation into its R&D efforts in a widely publicized buyout. And, not to be left behind, Ford acquired a majority stake in Argo AI, also for $1 billion.

That leaves us with a question: Do even the well-funded startups mentioned earlier stand a chance of either usurping market dominance from corporate incumbents or at least joining their ranks? Perhaps.

The reason why so much investor cash is going to these companies is because the market opportunity presented by autonomous vehicle technology is almost comically enormous. It’s not just a matter of the car market itself — projected to be over 80 million car sales globally in 2018 alone — but how we’ll spend all the time and mental bandwidth freed up by letting computers take the wheel. It’s no wonder that so many companies, and their backers, want even a tiny piece of that pie.

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Meet Alchemist Accelerator’s latest demo day cohort

An IoT-enabled lab for cannabis farmers, a system for catching drones mid-flight and the Internet of Cows are a few of the 17 startups exhibiting today at Alchemist Accelerator’s 18th demo day. The event, which will be streamed live here, focuses on big data and AI startups with an enterprise bent.

The startups are showing their stuff at Juniper’s Aspiration Dome in Sunnyvale, California at 3pm today, but you can catch the whole event online if you want to see just what computers and cows have in common. Here are the startups pitching onstage.

Tarsier – Tarsier has built AI computer vision to detect drones. The founders discovered the need while getting their MBAs at Stanford, after one had completed a PhD in aeronautics. Drones are proliferating. And getting into places they shouldn’t — prisons, R&D centers, public spaces. Securing these spaces today requires antiquated military gear that’s clunky and expensive. Tarsier is all software. And cheap, allowing them to serve markets the others can’t touch.

Lightbox – Retail 3D is sexy — think virtual try-ons, VR immersion, ARKit stores. But creating these experiences means creating 3D models of thousands of products. Today, artists slog through this process, outputting a few models per day. Lightbox wants to eliminate the humans. This duo of recent UPenn and Stanford Computer Science grads claim their approach to 3D scanning is pixel perfect without needing artists. They have booked $40,000 to date and want to digitize all of the world’s products.

Vorga – Cannabis is big business — more than $7 billion in revenue today and growing fast. The crop’s quality — and a farmer’s income — is highly sensitive to a few chemicals in it. Farmers today test the chemical composition of their crops through outsourced labs. Vorga’s bringing the lab in-house to the cannabis farmer via their IoT platform. The CEO has a PhD in chemical physics, and formerly helped the Department of Defense keep weapons of mass destruction out of the hands of terrorists. She’s now helping cannabis farmers get high… revenue.

Neulogic – Neulogic is founded by a duo of Computer Science PhDs that led key parts of Walmart.com product search. They now want to solve two major problems facing the online apparel industry: the need to provide curated inspiration to shoppers and the need to offset rising customer acquisition costs by selling more per order. Their solution combines AI with a fashion knowledge graph to generate outfits on demand.

Intensivate – Life used to be simple. Enterprises would use servers primarily for function-driven applications like billing. Today, servers are all about big data, analytics and insight. Intensivate thinks servers need a new chip upgrade to reflect that change. They are building a new CPU they claim gets 12x the performance for the same cost. Hardware plays like this are hard to pull off, but this might be the team to do it. It includes the former co-founder and CEO of CPU startup QED, which was acquired for $2.3 billion, and a PhD in parallel computation who was on the design team for the Alpha CPU from DEC.

Integry – SaaS companies put a lot of effort into building out integrations. Integry provides app creators their own integrations marketplace with pre-boarded partners so they can have apps working with theirs from the get go. The vision is to enable app creators to mimic their own Slack app directory without spending the years or the millions. Because these integrations sit inside their app, Integry claims setup rates are significantly better and churn is reduced by as much as 40 percent.

Cattle Care – AI video analytics applied to cows! Cattle Care wants to increase dairy farmers’ revenue by more than $1 million per year and make cows healthier at the same time. The product identifies cows in the barn by their unique black and white patterns. Algorithms collect parameters such as walking distance, interactions with other cows, feeding patterns and other variables to detect diseases early. Then the system sends alerts to farm employees when they need to take action, and confirms the problem has been solved afterwards.

VadR – VR/AR is grappling with a lack of engaging content. VadR thinks the cause is a broken feedback loop of analytics to the creators. This trio of IIT-Delhi engineers has built machine learning algorithms that get smarter over time and deliver actionable insights on how to modify content to increase engagement.

Tika – This duo of ex-Googlers wants to help engineering managers manage their teams better. Managers use Tika as an AI-powered assistant over Slack to facilitate personalized conversations with engineering teams. The goal is to quickly uncover and resolve employee engagement issues, and prevent talent churn.

GridRaster – GridRaster wants to bring AR/VR to mobile devices. The problem? AR/VR is compute-intensive. Latency, bandwidth and poor load balancing kill AR/VR on mobile networks. The solution? For this trio of systems engineers from Broadcom, Qualcomm and Texas Instruments, it’s about starting with enterprise use cases and building edge clouds to offload the work. They have 12 patents.

AitoeLabs – Despite the buzz around AI video analytics for security, AitoeLabs claims solutions today are plagued with hundreds of thousands of false alarms, requiring lots of human involvement. The engineering trio founding team combines a secret sauce of contextual data with their own deep models to solve this problem. They claim a 6x reduction in human monitoring needs with their tech. They’re at $240,000 ARR with $1 million of LOIs.

Ubiquios – Companies building wireless IoT devices waste more than $1.8 billion because of inadequate embedded software options making products late to market and exposing them to security and interoperability issues. The Ubiquios wireless stack wants to simplify the development of wireless IoT devices. The company claims their stack results in up to 90 percent lower cost and up to 50 percent faster time to market. Qualcomm is a partner.

4me, Inc. – 4me helps companies organize and track their IT outsourcing projects. They have 16 employees, 92 customers and generate several million in revenue annually. Storm Ventures led a $1.65 million investment into the company.

TorchFi – You know the pop-up screen you see when you log into a Wi-Fi hotspot? TorchFi thinks it’s a digital gold mine in the waiting. Their goal is to convert that into a sales channel for hotspot owners. Their first product is a digital menu that transforms the login screen into a food ordering screen for hotels and restaurants. Cisco has selected them as one of 20 apps to be distributed on their Meraki hotspots.

Cogitai – This team of 16 PhDs wants to usher in a more powerful type of AI called continual learning. The founders are the fathers of the field — and include professors in computer science from UT Austin and U Michigan. Unlike what we commonly think of as AI, Cogitai’s AI is built to acquire new skills and knowledge from experience, much like a child does. They have closed $2 million in bookings this year, and have $5 million in funding.

LoadTap – On-demand trucking apps are in vogue. LoadTap explicitly calls out that it is not one. This team, which includes an Apple software architect and founder with a family background in trucking, is an enterprise SaaS-only solution for shippers who prefer to work with their pre-vetted trucking companies in a closed loop. LoadTap automates matching between the shippers and trucking companies using AI and predictive analytics. They’re at $90,000 ARR and growing revenue 50 percent month over month.

Ondaka – Ondaka has built a VR-like 3D platform to render industrial information visually, starting with the oil and gas industry. For these industrial customers, the platform provides a better way to understand real-time IoT data, operational and job site safety issues and how reliable their systems are. The product launched two months ago, they have closed three customers already and are projecting ARR in the six figures. They have raised $350,000 in funding.

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The SEC creates an educational ‘token’ to stop scammers

“Travel is expensive, but we are at the cusp of a revolution that will democratize travel and leisure for everyone,” reads the breathless whitepaper for HoweyCoins. “The Internet was the first part of the revolution. The other part is blockchain technology and cryptocurrencies.”

“I’m all about HoweyCoins – this thing is going to pop at the top!” writes @boxingchamp1934, an official celebrity backer of the token. The website is full of beautiful beaches, features a handsome team of international men and women and the technology is nowhere to be seen, buried under a sea of excitement. The whitepaper is complete and well-written, focusing on the upside that is to come. Riches await if you invest in HoweyCoin, the latest ICO opportunity from trusted folks.

Or do they?

They don’t. All that breathless optimism is a site created by US Securities Exchange Commission to warn investors of scams and issues associated with token sales. The site features all the trademarks of a scammy security token, including tiered pre-sale pricing and an urgent countdown clock.

The site features a number of red flags that the SEC encourages users to watch out for, including, most importantly, claims that tokens can only go up in value. They write:

Every investment carries some degree of risk, which is reflected in the rate of return you can expect to receive. High returns entail high risks, possibly including a total loss on the investments. Most fraudsters spend a lot of time trying to convince investors that extremely high returns are “guaranteed” or “can’t miss.”

The SEC also notes that “it is never a good idea to make an investment decision just because someone famous says a product or service is a good investment,” and that it is never a good idea to invest with a credit card.

They also warn against pump and dump language found on many ICO pages. “Our past two pumps have doubled value for the period immediately after the pump for returns of over 225%,” wrote the HoweyCoins “creators,” a giant no-no in the world of investing.

You can read the rest of the red flags here.

While the site is fairly comical, it is sufficiently complete and would fool the casual observer. The SEC also posted a real-looking whitepaper that makes it clear that anyone can string together a few buzzwords and write a passable investment prospectus. That this is now a service available to anyone — for a price — makes things even scarier.

The site is part of the SEC’s outreach efforts to help investors understand ICOs.

“Strong investor protection is part of what makes American markets so strong…and striking the balance, [between innovation and investor protection] is very important,” said Chief of the SEC Cyber Unit Robert Cohen at Consensus this week. During the same panel the SEC claimed its doors were always open for questions.

Ultimately there is little separating the scams from the real token sales. This is a problem. The SEC is framing this problem in their own way based on decades of dealing with pink sheet pump and dumps and bogus get-rich-quick schemes. While HoweyCoins may not be real, there are plenty of scammers out there, and at least something like this bogus website makes it easier to spot the warning signs.

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The formula behind San Francisco’s startup success

Why has San Francisco’s startup scene generated so many hugely valuable companies over the past decade?

That’s the question we asked over the past few weeks while analyzing San Francisco startup funding, exit, and unicorn creation data. After all, it’s not as if founders of Uber, Airbnb, Lyft, Dropbox and Twitter had to get office space within a couple of miles of each other.

We hadn’t thought our data-centric approach would yield a clear recipe for success. San Francisco private and newly public unicorns are a diverse bunch, numbering more than 30, in areas ranging from ridesharing to online lending. Surely the path to billion-plus valuations would be equally varied.

But surprisingly, many of their secrets to success seem formulaic. The most valuable San Francisco companies to arise in the era of the smartphone have a number of shared traits, including a willingness and ability to post massive, sustained losses; high-powered investors; and a preponderance of easy-to-explain business models.

No, it’s not a recipe that’s likely replicable without talent, drive, connections and timing. But if you’ve got those ingredients, following the principles below might provide a good shot at unicorn status.

First you conquer, then you earn

Losing money is not a bug. It’s a feature.

First, lose money until you’ve left your rivals in the dust. This is the most important rule. It is the collective glue that holds the narratives of San Francisco startup success stories together. And while companies in other places have thrived with the same practice, arguably San Franciscans do it best.

It’s no secret that a majority of the most valuable internet and technology companies citywide lose gobs of money or post tiny profits relative to valuations. Uber, called the world’s most valuable startup, reportedly lost $4.5 billion last year. Dropbox lost more than $100 million after losing more than $200 million the year before and more than $300 million the year before that. Even Airbnb, whose model of taking a share of homestay revenues sounds like an easy recipe for returns, took nine years to post its first annual profit.

Not making money can be the ultimate competitive advantage, if you can afford it.

Industry stalwarts lose money, too. Salesforce, with a market cap of $88 billion, has posted losses for the vast majority of its operating history. Square, valued at nearly $20 billion, has never been profitable on a GAAP basis. DocuSign, the 15-year-old newly public company that dominates the e-signature space, lost more than $50 million in its last fiscal year (and more than $100 million in each of the two preceding years). Of course, these companies, like their unicorn brethren, invest heavily in growing revenues, attracting investors who value this approach.

We could go on. But the basic takeaway is this: Losing money is not a bug. It’s a feature. One might even argue that entrepreneurs in metro areas with a more fiscally restrained investment culture are missing out.

What’s also noteworthy is the propensity of so many city startups to wreak havoc on existing, profitable industries without generating big profits themselves. Craigslist, a San Francisco nonprofit, may have started the trend in the 1990s by blowing up the newspaper classified business. Today, Uber and Lyft have decimated the value of taxi medallions.

Not making money can be the ultimate competitive advantage, if you can afford it, as it prevents others from entering the space or catching up as your startup gobbles up greater and greater market share. Then, when rivals are out of the picture, it’s possible to raise prices and start focusing on operating in the black.

Raise money from investors who’ve done this before

You can’t lose money on your own. And you can’t lose any old money, either. To succeed as a San Francisco unicorn, it helps to lose money provided by one of a short list of prestigious investors who have previously backed valuable, unprofitable Northern California startups.

It’s not a mysterious list. Most of the names are well-known venture and seed investors who’ve been actively investing in local startups for many years and commonly feature on rankings like the Midas List. We’ve put together a few names here.

You might wonder why it’s so much better to lose money provided by Sequoia Capital than, say, a lower-profile but still wealthy investor. We could speculate that the following factors are at play: a firm’s reputation for selecting winning startups, a willingness of later investors to follow these VCs at higher valuations and these firms’ skill in shepherding portfolio companies through rapid growth cycles to an eventual exit.

Whatever the exact connection, the data speaks for itself. The vast majority of San Francisco’s most valuable private and recently public internet and technology companies have backing from investors on the short list, commonly beginning with early-stage rounds.

Pick a business model that relatives understand

Generally speaking, you don’t need to know a lot about semiconductor technology or networking infrastructure to explain what a high-valuation San Francisco company does. Instead, it’s more along the lines of: “They have an app for getting rides from strangers,” or “They have an app for renting rooms in your house to strangers.” It may sound strange at first, but pretty soon it’s something everyone seems to be doing.

It’s not a recipe that’s likely replicable without talent, drive, connections and timing. 

list of 32 San Francisco-based unicorns and near-unicorns is populated mostly with companies that have widely understood brands, including Pinterest, Instacart and Slack, along with Uber, Lyft and Airbnb. While there are some lesser-known enterprise software names, they’re not among the largest investment recipients.

Part of the consumer-facing, high brand recognition qualities of San Francisco startups may be tied to the decision to locate in an urban center. If you were planning to manufacture semiconductor components, for instance, you would probably set up headquarters in a less space-constrained suburban setting.

Reading between the lines of red ink

While it can be frustrating to watch a company lurch from quarter to quarter without a profit in sight, there is ample evidence the approach can be wildly successful over time.

Seattle’s Amazon is probably the poster child for this strategy. Jeff Bezos, recently declared the world’s richest man, led the company for more than a decade before reporting the first annual profit.

These days, San Francisco seems to be ground central for this company-building technique. While it’s certainly not necessary to locate here, it does seem to be the single urban location most closely associated with massively scalable, money-losing consumer-facing startups.

Perhaps it’s just one of those things that after a while becomes status quo. If you want to be a movie star, you go to Hollywood. And if you want to make it on Wall Street, you go to Wall Street. Likewise, if you want to make it by launching an industry-altering business with a good shot at a multi-billion-dollar valuation, all while losing eye-popping sums of money, then you go to San Francisco.

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ICOs like to move fast and break (lots of) things

Startup life is full of quick, lateral thinking. “Move fast and break things” is the mantra. However, with the rise of token sales – essentially vehicles for untested startups to raise millions in a few minutes – lots of stuff gets broken and little gets fixed.

Take BCT – the Blockchain Terminal – for example. This frothy project led by Bob Bonomo, a former hedge fund guy turned Blockchain guru, features some interesting breakages.

Yesterday at about 3pm Eastern Time the company’s FAQ – which has since been updated but is still hidden here – read something like this:

While this sort of techno greeking is fine if you’re sending mock-ups back and forth, the token sale had been running since April 1st, a fact that was baffling to me and another reporter. Was this an April Fool’s joke? No, because when I visited the sale’s Telegram room I found a group of happy buyers asking questions about their future tokens.

Ever the reporter, I asked if anyone had seen the terminals and a community manager sent me this:

Interesting… blank screens at a demo event. The other CM, quicker on the draw, sent this:

Fair enough. In fact, crypto needs a product like this to legitimize it with Wall Street. But clearly they were moving so fast that the wheels were falling off.

Finally I did the obvious thing: visit the white paper. There we find that the Terminal is being built in conjunction with FactSet, a venerable research company that has seen all the vicissitudes of financial data. In fact, the paper is a tour-de-force on par with the best of the white papers I’ve seen. But we also discover that the white paper is a draft.

In short, BCT wouldn’t pass the average human investor sniff test but is definitely well on the way to completing its token sale. This is a problem.

BCT is not alone. I’ve spoken to development houses working with founders who barely understand cryptocurrency let alone understand their own token sales. I’ve seen founders’ eyes light up like the Big Bad Wolf eyeing Porky Pig when they talk about all the capital they will unlock. And I spoke to a founder on stage who said he would be very careful with the $80 million they raised for a company designed to raise money for ICOs. Greed is clouding this market in ways that are at once dangerous and comical.

There is precedent for this. In the early days of the Internet and even the frothiest dot-com days you could see the avarice in the eyes of Pets.com and Cisco executives who knew that big money was just around the corner. And we can’t begrudge these founders their excitement. What founder wouldn’t want the sweet feeling of being fully funded for, we presume, the next decade?

I’ve been following token sales with great interest over the past few months for a few reasons. First, I understand the hype cycle. I’ve seen tactics used by token sellers used before by hardware sellers, most notably with flops like the Phantom gaming console and the Notion Ink Adam, and there is a stink that permeates projects that are, at best, half-baked.

I want token sales to thrive as a method to raise capital. I want small startups to be able to turn on a spigot previously available to the well-connected and well-heeled. But the exact opposite seems true. Bankers are moving into a technology space that they little understand while carpetbaggers – lawyers, PR folks, advisors – are working hard to extract cash out of these windfalls. In the end the token sale industry should formalize itself and become as boring as the VC industry. I just hope it survives long enough to get there.

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Aurora hires SpaceX’s Jinnah Hosein, opens SF and Pittsburgh offices

Self-driving technology company Aurora has made some key moves on its leadership team and overall company growth: It’s bringing on SpaceX’s now former head of software engineering, Jinnah Hosein, to lead its own software engineering team in a VP role. The autonomous software provider is also opening up two new offices, including one in San Francisco, and another in Pittsburgh, in addition to its existing HQ in Palo Alto.

Bringing on Hosein is a huge move for Aurora, which will now have some additional senior leadership taken to help direct and organize its growing engineering team, according to Aurora co-founder Chris Urmson . Hosein’s background includes his time as VP of Software Engineering at SpaceX, where he spent the past four years and oversaw projects including the recent successful Falcon Heavy launch. Before that, he was Director of Software Engineering at Google working on Google Cloud, site reliability and other software projects.

“It’s a pretty incredible set of experiences he has,” Urmson said. “We’re just excited about him bringing that leadership capability, that experience in building both cloud and incredibly reliable software to our team and working with the rest of the folks here.”

Hosein also worked for a brief time overseeing Tesla’s software operations as well as SpaceX’s when he served as acting VP of Tesla’s Autopilot Software prior to Tesla hiring Apple’s Chris Lattner for the role. Urmson says that Hosein’s proven track record launching rockets, and organizing software projects on that level of complexity is more important to Aurora than any brief time he may have spent on Autopilot, however.

Aurora is also opening two new physical offices and testing locations, as mentioned, including the San Francisco one that Urmson says will be a welcome relief to some of their employees currently commuting south to Palo Alto, as well as a way to attract more talent looking to work in the city proper. The Pittsburgh office gives them a new testbed, where they can prove their tech in inclement driving conditions and adverse winter weather, and it also puts them in close proximity to Carnegie Mellon and Pittsburgh’s robotics talent pool.

“When you combine that, between the offices we have in the South Bay, the San Francisco test areas that we’ll now have more access to and the Pittsburgh test areas, we have a pretty exciting diversity of test environments and places to operate,” Urmson added.

Aurora has already announced partnerships with Volkswagen, Hyundai, Byton and more, and recently added LinkedIn founder Reid Hoffman and Index Ventures’ Mike Volpi to its board.

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The new Light Phone 2 keeps things basic but adds e-ink and ‘essentials’

 Light is back with a new twist on its anti-smartphone phone. But this time, instead of doing just one thing, the Light Phone 2 does a few, and exists somewhere between the original Light and your overwrought iPhone – though still far closer to the first-generation Light phone overall. The new design features a matte finish e-ink display, which occupies most fo the front face of the… Read More

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